Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance
针对连续时间随机波动率模型，利用日度收盘价和极差数据，发现标准模型拟合不佳，提出一个三因子模型以捕捉波动率的长记忆特征，并开发了估计远期已实现方差条件分布的方法。
A common model for security price dynamics is the continuous-time stochastic volatility model. For this model, Hull and White (1987) show that the price of a derivative claim is the conditional expectation of the Black-Scholes price with the forward integrated variance replacing the Black-Scholes variance. Implementing the Hull and White characterization requires both estimates of the price dynamics and the conditional distribution of the forward integrated variance given observed variables. Using daily data on close-to-close price movement and the daily range, we find that standard models do not fit the data very well and that a more general three-factor model does better, as it mimics the long-memory feature of financial volatility. We develop techniques for estimating the conditional distribution of the forward integrated variance given observed variables. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology